Muhammad Rangga Hata R

At Otoritas Jasa Keuangan, I aggregated daily stock-market data for more than 500 listed companies and organized regional GDP data covering more than 70 cities and regencies in Sulawesi. I also cleaned and maintained securities records from the KSEI Masterfile using Microsoft Excel.

My end-to-end analysis project for the fictitious Seduh Coffee Brand used SQL, Python, and Excel to examine sales, customer, and marketing data. I applied RFM segmentation and identified findings including Roasted Beans as the most profitable product category and Shopee as the most valuable sales channel.

For my bachelor's thesis at Institut Teknologi Sepuluh Nopember, I developed a stock-price forecasting approach combining Self-Organizing Feature Map and Support Vector Regression, using historical data from PT Bank Central Asia Tbk (BBCA). I evaluated the forecasts with MAE and MAPE.

I have a mathematics degree and a Wakil Manajer Investasi qualification. My experience includes financial and economic data processing, statistical analysis, and machine-learning methods.